About me

I am a postdoctoral researcher specializing in machine learning, Bayesian inference, and high-performance computing, applied to large-scale scientific datasets. My work sits at the intersection of ML and physics: I build neural network surrogate models, probabilistic inference pipelines, and GPU-accelerated software to extract insight from cosmological observations and simulations.

I am currently a postdoc in the Cosmological Physics and Advanced Computing (CPAC) group at Argonne National Laboratory, where I develop and deploy ML-powered models in JAX on multi-node, multi-GPU HPC systems at the Argonne Leadership Computing Facility (ALCF). My current focus is building fast, differentiable surrogate models that replace expensive physics simulations, enabling large-scale Bayesian inference workflows that would otherwise be computationally prohibitive.

Before Argonne, I completed my PhD at the Laboratoire de Physique Subatomique et Cosmologie (Université Grenoble Alpes, France), where I built end-to-end data analysis pipelines for high-resolution telescope observations, notably achieving a ~1000× speedup through software reengineering and algorithmic optimization and leading developments of Monte-Carlo simulation frameworks to validate analysis pipelines.

My research application is cosmology I study galaxy clusters, the largest gravitationally bound structures in the Universe, as probes of fundamental physics. The challenges cosmological analyses present have led me to develop tools in ML, Bayesian statistics, and scientific software engineering.